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We provide non-asymptotic excess risk guarantees for statistical learning in a setting where the population risk with respect to which we evaluate the target parameter depends on an unknown nuisance parameter that must be estimated from data.
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Efficient estimation of models with conditional moment restrictions containing unknown functions
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Instrumental variable estimation of nonparametric models
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Unified cross-validation methodology for selection among estimators and a general cross-validated adaptive epsilon-net estimator: Finite sample oracle inequalities and examples
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Nonparametric methods for inference in the presence of instrumental variables
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A general imputation methodology for nonparametric regression with censored data
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E. L. Lehmann and G. Casella · 2006
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M. J. van der Laan, S. Dudoit, and A. van der Vaart · 2006
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S. Shalev-Shwartz and S. Ben-David · 2014
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Statistical learning with sparsity: the lasso and generalizations
T. Hastie, R. Tibshirani, and M. Wainwright · 2015
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Learning with square loss: Localization through offset rademacher complexity
T. Liang, A. Rakhlin, and K. Sridharan · 2015
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Logistic regression: The importance of being improper
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Size-independent sample complexity of neural networks
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